BG-REAL is a public benchmark for detecting and localizing background manipulations in images, built from Open Images V7 with 7,000 processed samples across 1,200 source groups. It includes 6,000 real-data anchored samples, 1,000 synthetic controls, six edit families, matched authentic controls, and extensive quality‑control measures. The release offers a construction pipeline, evaluation protocol, and three external baseline results, highlighting false‑positive rates due to re‑encoding artifacts.
arXiv:2609.37783v1 Announce Type: cross Abstract: Photographic evidence is becoming increasingly vulnerable to forms of alteration and fabrication that existing legal and technical workflows are not...
ManiVid introduces a unified forensic analysis framework for manipulated videos, combining forgery detection, artifact grounding, and anomaly explanation. The authors release ManiVid-38K, a large dataset of 19K real‑fake video pairs with authenticity labels, forgery masks, and explanations, and a benchmark ManiVidBench with 1K balanced pairs. ManiVidLens, the proposed model, outperforms existing methods in artifact grounding and anomaly explanation while matching state‑of‑the‑art detection accuracy.
Photographic evidence is becoming increasingly vulnerable to forms of alteration and fabrication that existing legal and technical workflows are not well equipped to evaluate. Surveillance frames, das...
arXiv:2610.01778v1 Announce Type: new Abstract: Reliable evaluation of image forgery localization (IFL) requires assessing models under diverse distribution changes, yet existing benchmarks often cov...
DailyBench is a unified benchmark designed to evaluate AI-generated image detectors on both modern full-image synthesis and object-level manipulation. It comprises two subsets: FakeBench, featuring high‑quality images from recent open‑source and commercial generative models, and ManipulationBench, containing subtle local edits applied to real images using advanced image‑conditional models. Experiments show that detectors with high accuracy on older datasets perform poorly on DailyBench, revealing significant robustness gaps.
arXiv:2607. 26641v1 Announce Type: cross Abstract: Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns.
arXiv:2609.36882v1 Announce Type: new Abstract: Localizing AI-edited regions is essential for interpretable forensic analysis, but remains challenging due to subtle and spatially distributed artifact...
arXiv:2608. 04840v1 Announce Type: cross Abstract: Verifying the authenticity of satellite imagery has become increasingly critical given advances in generative artificial intelligence.
arXiv:2607.25962v2 Announce Type: replace Abstract: Recent generative models can produce images with few obvious visual artifacts, weakening detectors and explanations that rely only on surface appea...
The paper introduces TaintedPixels, a proactive video‑protection technique that embeds subtle, structured perturbations into the blue channel of facial regions. These perturbations remain invisible in the original video but become noticeable after black‑box manipulation, thereby flagging deepfakes. Experiments on three off‑the‑shelf manipulation tools and two detectors show that TaintedPixels achieves the highest forgery detection rate while keeping visual distortion minimal (LPIPS = 0.0042), and a human study confirms that protected videos are rarely suspected, whereas forgeries from protected sources are identified as fake far more often than those from unprotected sources.
arXiv:2607. 18230v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have significantly improved image generation and editing capabilities, making pixel-level image tampering detection increasingly important yet challenging under cross-model and out-of-distribution shifts.
The paper explores how partial C2PA-based Content Credentials (CCs) can be used to improve image forensics. It proposes a two-step method: first reconstructing the last operations applied to an image by analyzing its partial CCs, and second adapting forensic detectors to the image’s statistical characteristics based on that reconstruction. Experiments on commercial C2PA-enabled tools demonstrate the feasibility of this reconstruction and show significant performance gains in synthetic image detection after post‑processing.